Noise reduction processing method and apparatus for a biological tissue image

ABSTRACT

Noise reduction processing for measured spectrum data is performed without any information loss due to discrete data characteristics of the measured spectrum data. Optical spectra in one or more cross-sections are measured through use of a signal correlated with a substance distributed in a biological tissue, and a biological tissue image having reduced noise is reconstructed from the spectra.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a noise reduction processing method fora biological tissue image and an apparatus therefor. Specifically, thepresent invention relates to a method and apparatus for reconstructing abiological tissue image having reduced noise components from measuredspectrum data of a biological tissue. The present invention also relatesto an image display for clearly displaying a diseased site inpathological diagnosis through use of the thus acquired biologicaltissue image.

2. Description of the Related Art

There has been performed pathological diagnosis, that is, observing abiological tissue with a microscope or the like and diagnosing thepresence or absence of a lesion and a type of the lesion based on theobservation. The pathological diagnosis requires visualization of aconstituent substance or contained substance correlated with abiological tissue to be observed. A technique for staining a specificantigen protein through use of an immunostaining method has mainly beenemployed in the pathological diagnosis. When breast cancer is taken asan example, an estrogen receptor (ER) (serving as a judgment criterionfor a hormone therapy), which is expressed in a hormone-dependent tumor,and a membrane protein HER2 (serving as a judgment criterion forHerceptin administration), which is found in a fast-growing malignantcancer, are visualized by the immunostaining method. However, theimmunostaining method involves problems that its reproducibility is poorbecause an antibody is unstable and antigen-antibody reaction efficiencyis difficult to control. Further, in the future, when there is anincreasing need for such functional diagnosis, for example, when therearises a need for detection of several tens or more kinds of constituentsubstances or contained substances, currently-employed immunostainingmethods cannot meet the need any more.

Further, in some cases, the visualization of the constituent substanceor contained substance may be required at a cellular level, not at atissue level. For example, in research on cancer stem cells, it wasrevealed that a tumor was formed in only part of fractions of a tumortissue after xenotransplantation to immunocompromised mice. Therefore,it is being understood that growth of a tumor tissue, in which cancerstem cells are recognized, depends on differentiation and self-renewalabilities of the cancer stem cells. In such research, it is necessary toobserve an expression distribution of a constituent substance orcontained substance in an individual cell in a tissue, not the entiretissue.

As described above, in the pathological diagnosis, a constituentsubstance or contained substance correlated with a tumor tissue or thelike is required to be exhaustively visualized at a cellular level.There are given, as candidates of a method for the visualization,secondary ion mass spectrometry (SIMS), such as time-of-flight secondaryion mass spectrometry (TOF-SIMS), and Raman spectroscopy. In measurementby the SIMS or Raman spectroscopy, information at each point (region) ina space can be obtained with a high spatial resolution. That is, spatialdistribution information on each peak value for a measured spectrumcorrelated with an object to be measured is obtained. Consequently, aspatial distribution of a substance in a biological tissue correlatedwith the measured spectrum can be determined.

The SIMS is a method involving irradiating a sample with a primary ionbeam, and detecting a secondary ion emitted from the sample, therebyobtaining a mass spectrum at each point on the sample. For example, inTOF-SIMS, through utilization of the fact that a time-of-flight of asecondary ion depends on a mass m and charge z of the ion, the secondaryion is identified, and thereby a mass spectrum at each point on a samplecan be obtained.

The Raman spectroscopy involves acquiring a Raman spectrum byirradiating a substance with a laser beam, which is monochromatic light,as a light source, and detecting the generated Raman scattered lightwith a spectrometer or an interferometer. A difference (Raman shift)between a frequency of the Raman scattered light and a frequency ofincident light has a value peculiar to a structure of a substance.Hence, a Raman spectrum specific for an object to be measured can beacquired.

As used herein, the “cellular level” means a level at which at least anindividual cell can be identified. A diameter of the cell falls within arange of approximately 10 μm to 20 μm (except that a large cell such asa nerve cell has a diameter of about 50 μm). Thus, in order to acquire atwo-dimensional distribution image at a cellular level, the spatialresolution needs to be 10 μm or less, preferably 5 μm or less, morepreferably 2 μm or less, still more preferably 1 μm or less. The spatialresolution may be determined from, for example, results of linearanalysis of a knife-edge sample. That is, the spatial resolution isdetermined based on the following general definition: “a distancebetween two points at which signal intensities attributed to a substanceof interest near the boundary of a sample are 20% and 80%,respectively.”

In order to acquire biological information from measured spectrum, forexample, it is necessary to generate a classifier by machine learning inadvance and to apply the classifier to measured spectrum data of asample (Japanese Patent Application Laid-Open No. 2010-71953). However,when its signal intensity is low, it is impossible to disregardinfluences of noise components on the classification processing. Hence,it is necessary to appropriately reduce noise components each having alow correlation with an original signal of a biological tissue. As usedherein, machine learning refers to a technique involving empiricallylearning previously acquired data, and interpreting newly acquired databased on the learning results. The classifier refers to judgmentcriterion information to be generated by empirically learning arelationship between previously acquired data and biologicalinformation.

Various noise reduction techniques are known. Japanese PatentApplication Laid-Open No. 2007-209755 proposes a technique for reducingnoise effectively by analyzing two or more two-dimensional imagesthrough use of wavelet analysis, and considering a correlation betweenboth the images. S. G. Nikolov et al., “De-noising of SIMS images viawavelet shrinkage,” Chemometrics and Intelligent Laboratory Systems,vol. 34 (1996), p. 263-273 proposes a noise reduction technique inconsideration of a probability process (Gauss or Poisson process)involving using two-dimensional wavelet analysis for an SIMS image.

SUMMARY OF THE INVENTION

In noise reduction processing having applied thereto Fourier analysis orwavelet analysis, an image having reduced noise has been obtained byspecifying a basis function, removing subthreshold components, andperforming inverse transform. However, spectrum data has a discretedistribution having a large number of peak values. Hence, when atrigonometric function is used as in the Fourier analysis, an originalsignal component may be removed. Even in the case of using the waveletanalysis, there is a problem in that noise components are notappropriately removed unless an appropriate basis function is selected.

In view of the foregoing, the present invention utilizes, in the caseof, for example, measuring a biological tissue, the fact that spectrumdata information based on the total sum of spectra specific for anobject to be measured is obtained. That is, spectrum data obtained bymeasurement, when expressed through use of the sum of typical spectraspecific for an object to be measured, can be divided into the sum ofspectra characteristic of the object to be measured and noise componentsexcept the spectra (hereinafter, the spectra are referred to as “typicalspecific spectra”). Thus, an image having reduced noise can be acquiredby removing the noise components and reconstructing spectrum signalsthrough use of typical specific spectra derived from a biological tissue(hereinafter, the procedure is referred to as “image reconstruction”).

According to the present invention, there is provided a method ofacquiring a biological tissue image, including measuring spectra havinga spatial distribution of a biological tissue and acquiring a biologicaltissue image having reduced noise from the measured spectra, in whichthe reduction of the noise is performed through use of reference datafor the spectrum data.

According to the present invention, further, there is provided anapparatus for acquiring a biological tissue image, the apparatus beingconfigured to measure spectra having a spatial distribution of abiological tissue and acquire a biological tissue image having reducednoise from the measured spectra, in which the reduction of the noise isperformed through use of reference data for the spectrum data.

According to the present invention, it is possible to perform noisereduction processing for spectrum data without causing any informationloss due to discrete data characteristics of the spectrum data.Consequently, classification of a biological tissue is enabled withhigher accuracy than ever before, and hence the present invention isuseful for an application to pathological diagnosis or the like.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an apparatus according to the presentinvention.

FIGS. 2A, 2B, and 2C are schematic diagrams of spectrum signals havingan intensity distribution in a two-dimensional plane.

FIG. 3 is a flowchart of the present invention.

FIG. 4 schematically illustrates the division of measured signals intorespective specific spectra.

FIG. 5 schematically illustrates the reconstruction of signals havingnoise components removed therefrom.

FIG. 6 is a flowchart of a process for dividing the entire spectrum intotypical specific spectra.

FIGS. 7A and 7B are schematic diagrams of decision tree algorithms.

FIGS. 8A and 8B schematically illustrate a series of processes of thepresent invention.

FIG. 9 schematically illustrates the appearance frequency counting ofspecific spectra.

FIGS. 10A, 10B, and 10C schematically illustrate a process for applyingthe Fisher's linear discriminant method.

FIGS. 11A, 11B, and 11C are images showing application effects ofExample 1 of the present invention.

FIGS. 12A and 12B are images (enlarged images) showing the applicationeffects of Example 1 of the present invention.

FIGS. 13A and 13B are images showing application effects of Example 2 ofthe present invention.

DESCRIPTION OF THE EMBODIMENTS

The present invention is characterized in that, in measuring spectrahaving a spatial distribution of a biological tissue and reconstructinga biological tissue image having reduced noise from the measuredspectra, the reduction of the noise is performed through use ofreference data for the spectrum data. In the present invention, thereference data can be generated through utilization of training data. Inaddition, in the present invention, the reduction of the noise can beperformed by generating a classifier through utilization of the trainingdata, dividing an entire spectrum into typical specific spectra throughuse of the classifier, and reconstructing an image from the typicalspecific spectra derived from the biological tissue.

The measured spectra can be, but not limited to, optical spectrum in arange of ultraviolet, visible or infrared light, raman spectrum and massspectrum, etc.

In an embodiment of the present invention, when a two-dimensional massspectrum of a biological tissue section is measured by mass spectrometryusing a primary probe selected from the group consisting of an ion, anelectron, a neutral particle, and a laser beam, and a biological tissueimage having reduced noise is acquired from the two-dimensional massspectrum, the reduction of the noise is performed through use ofreference data for the mass spectrum.

Hereinafter, embodiments of the present invention are specificallydescribed with reference to the flowcharts and other drawings. It shouldbe noted that the following specific example is an example of the bestembodiment according to the present invention, but the present inventionis by no means limited to any such specific embodiment. The presentinvention, which includes measuring a sample having a compositiondistribution in a space, is applicable to results obtained by anymeasurement method as long as positional information at each point(region) in the space and measured spectrum information corresponding tothe position of each point are obtained. The space can be, but notlimited to, respective regions of a biological tissue.

FIG. 2A, FIG. 2B, and FIG. 2C illustrate schematic diagrams of measuredspectra measured at each point on a space. For example, when thetwo-dimensional plane of FIG. 2A is considered as a space in whichsignals are acquired, information to be obtained is three-dimensionaldata. When each point in a three-dimensional space in generating thethree-dimensional data is expressed by coordinates (X, Y, Z), thecomponents X and Y are coordinates on a two-dimensional space (XYplane), in which measured spectrum signals have been obtained, asexemplarily illustrated in FIG. 2B for the component X. The component Zis a measured spectrum signal at each point on the XY plane, asillustrated in FIG. 2C. Thus, the components X and Y contain theX-coordinate and Y-coordinate of the point where a signal has beenmeasured, respectively, and the component Z contains a value for ameasured signal corresponding to the intensity of each peak component.

FIG. 3 illustrates a flowchart of noise reduction processing in thepresent invention. The following description is made with reference tothe drawing according to the order in the flowchart.

In Step S101 of FIG. 3, measured spectrum data is divided into typicalspecific spectra (Expression 1). As used herein, the typical specificspectra refer to spectra specific for respective components constitutingthe entire spectrum. In order to determine the typical specific spectra,for example, a correlation (inner product) between each spectrumcomponent prepared in advance and measured spectrum data has only to becalculated. Otherwise, the typical specific spectra may be determinedthrough utilization of training data or the like. FIG. 4 schematicallyillustrates the division of measured signals into respective spectrumcomponents. As used herein, the training data means data acquired beforethe acquisition of new data.Measured data=a×Specific spectrum A+b×Specific spectrum B+c×Specificspectrum C+ . . . +a _(s)×Common peak A+b _(s)×Common peak B+ . . .+n×noise components  Expression (1)

In Step S102 of FIG. 3, a component except typical specific spectrumcomponents derived from a biological tissue is set to zero. Next, inStep S103, a biological tissue image having reduced noise is acquired byreconstructing signals through use of the typical specific spectrumcomponents derived from the biological tissue (Expression 2). FIG. 5schematically illustrates the reconstruction of the signals.Measured data=a×Specific spectrum A+b×Specific spectrum B+c×Specificspectrum C+ . . . +a _(s)×Common peak A+b _(s)×Common peakB+  Expression (2)

FIG. 6 illustrates an example of a flowchart for determining typicalspecific spectra through use of training data. The following descriptionis made with reference to the drawing according to the order in theflowchart.

In Step S201 of FIG. 6, a peak to be used for determining typicalspecific spectra is selected. Next, in Step S202, the data isstandardized and digitalized. In Step S203, the standardized anddigitalized data is divided into typical specific spectra by, forexample, machine learning. In this step, for example, an appearancefrequency can be counted or the inside of a feature space can be dividedinto regions of respective specific spectra. As used herein, the featurespace refers to a space in which a feature value is projected in orderto classify the attribute of data, and the feature value refers to avalue suitable for classification to be generated from original data. Inthis case, a standardized peak intensity or the like can be consideredas the feature value. There may be employed, as a technique for themachine learning, for example, the Fisher's linear discriminant method,a Support Vector Machine (SVM), a decision tree, or a random forestmethod in consideration of an ensemble average thereof. Hereinafter, acase of employing the decision tree and a case of employing the Fisher'slinear discriminant method are described as examples of supervisedmachine learning.

FIG. 7A and FIG. 7B illustrate a process for counting appearancefrequencies of typical specific spectra by decision tree algorithms. Thepresence and absence of a certain peak component can be expressed by 1and 0, respectively, and hence the presence and absence of a pluralityof peak components can be expressed by a decision tree includinghierarchical binary trees (in this case, the number of peaks to be usedequals the number of hierarchies). As used herein, the binary treerefers to data expressed in a branched structure. Respective spectra tobe learned are accompanied by identification numbers (labels) forbiological tissues, such as 1 for a cancer tissue and 0 for a normaltissue, as supervisory data. In the case of expressing measured spectrumdata by the decision tree, a selection of a peak component to be firstexpressed is an important issue (FIG. 7A). In this case, since itspurpose is efficient classification into the same label, entropy isrecursively evaluated and such a decision tree that can reduce entropymost efficiently is finally determined (FIG. 7B). In this connection,the entropy is defined by Expression (3), and a decrease in the entropycorresponds to the classification of a set of mixed data accompanied bydifferent labels into a set of data accompanied by the same label. InExpression (3), i means a node number of a branch portion of a decisiontree, and p means a partition probability (at each node, percentages ofrespective labels).

$\begin{matrix}{- {\sum\limits_{i = 0}^{n}\{ {{{p( 0 \middle| i )}\log\;{p( 0 \middle| i )}} + {{p( 1 \middle| i )}\log\;{p( 1 \middle| i )}}} \}}} & {{Expression}\mspace{14mu}(3)}\end{matrix}$

FIG. 8A and FIG. 8B schematically illustrate the series of processesillustrated in the flowcharts of FIGS. 3 and 6. In FIG. 8A, theappearance frequencies of typical specific spectra are counted bymachine learning. In FIG. 8B, based on the appearance frequencies, themeasured signals are divided into respective typical specific spectra.It should be noted that when peak components common to the respectivetypical specific spectra are present, the peak components are separatelyhandled as common peaks. Further, the respective typical specificspectra and common peaks are standardized so as to achieve a norm (innerproduct) of 1. Further, FIG. 9 schematically illustrates the divisioninto specific spectra according to the order of the appearancefrequencies. As described above, the appearance frequencies are countedfrom training data to determine typical specific spectra, and hence thetypical specific spectra can be utilized as reference data.

FIG. 10A, FIG. 10B, and FIG. 10C illustrate a process for separatingtypical specific spectra from the entire measured spectrum data by theFisher's linear discriminant method. The region in the white frame ofFIG. 10A represents a region from which measured spectrum data to beused as training data is acquired. FIG. 10B is a schematic diagram ofthe measured spectrum data to be used. Respective spectra to be learnedare accompanied by identification numbers (labels) for the measuredspectrum data, such as 1 for a cancer tissue, 0 for a normal tissue, and2 for noise components, as supervisory data. FIG. 10C schematicallyillustrates the state of projecting feature values acquired from themeasured spectrum data onto a feature space (classification space) anddetermining an optimum borderline by the Fisher's linear discriminantmethod. In this case, a standardized peak intensity or the like can beconsidered as the feature value. The Fisher's linear discriminant methodinvolves determining such an axis as to maximize a ratio between thebetween-group variance and within-group variance of a projectioncomponent with respect to the axis, and such axis is given by Expression(4), for example, when two groups including group 1 and group 2 areconsidered. In Expression (4), x represents a coordinate in a featurespace, and the position at which the sign of H(x) changes is a borderfor distinguishing both the groups from each other. Vectors x₁ and x₂ inExpression (4) mean sample mean vectors of the respective groups(Expression (6)), and matrices S₁ and S₂ in Expression (5) mean samplevariance covariance matrices of the respective groups (Expression (7))(expressions in the case where the feature space is two-dimensional). n₁and n₂ represent the numbers of data in the respective groups.

$\begin{matrix}{{h(x)} = {{( {{\overset{\_}{x}}_{1} - {\overset{\_}{x}}_{2}} )^{T}S^{- 1}x} - {\frac{1}{2}( {{\overset{\_}{x}}_{1} - {\overset{\_}{x}}_{2}} )^{T}{S^{- 1}( {{\overset{\_}{x}}_{1} + {\overset{\_}{x}}_{2}} )}}}} & {{Expression}\mspace{14mu}(4)} \\{S = {\frac{1}{n_{1} + n_{2} - 2}\{ {{( {n_{1} - 1} )S_{1}} + {( {n_{2} - 1} )S_{2}}} \}}} & {{Expression}\mspace{14mu}(5)} \\{{\overset{\_}{x}}_{1} = {{\begin{pmatrix}{\overset{\_}{x}}_{1}^{(1)} \\{\overset{\_}{x}}_{2}^{(1)}\end{pmatrix}\mspace{14mu}{\overset{\_}{x}}_{2}} = \begin{pmatrix}{\overset{\_}{x}}_{1}^{(2)} \\{\overset{\_}{x}}_{2}^{(2)}\end{pmatrix}}} & {{Expression}\mspace{14mu}(6)} \\{S_{1} = {{\begin{pmatrix}s_{11}^{(1)} & s_{12}^{(1)} \\s_{21}^{(1)} & s_{22}^{(1)}\end{pmatrix}\mspace{14mu} S_{2}} = \begin{pmatrix}s_{11}^{(2)} & s_{12}^{(2)} \\s_{21}^{(2)} & s_{22}^{(2)}\end{pmatrix}}} & {{Expression}\mspace{11mu}(7)}\end{matrix}$

It should be noted that in FIG. 10C, an classification axis 1 is an axisfor separating a biological tissue 1 from noise components, and anclassification axis 2 is an axis for separating a biological tissue 2from the biological tissue 1. The classification axis 1 can separatespectra specific for a biological tissue from the noise components.

The division into typical specific spectra and common spectra not onlycan suitably reduce noise except original biological tissue componentsbut also can compress information. In this case, only a spectrumcomponent having a high appearance frequency is held by setting thecontribution of a spectrum having a volume equal to or less than acertain value to zero in a spectrum or feature space having a frequencyequal to or less than a certain value. The information compressionprocessing is particularly effective in the case of storing a largequantity of measured spectrum data.

The present invention can be realized by an apparatus that implementsthe above-mentioned specific embodiment. FIG. 1 illustrates theconfiguration of the entire apparatus according to the presentinvention. A sample on a substrate is represented by reference numeral1, and a signal detector is represented by reference numeral 2. A signalprocessor for subjecting acquired signals to the above-mentionedprocessing is represented by reference numeral 3, and an image displayfor displaying the results of the signal processing on a screen isrepresented by reference numeral 4.

EXAMPLE 1

Hereinafter, Example 1 of the present invention is described. In thisexample, through use of a TOF-SIMS 5 type apparatus (trade name)manufactured by ION-TOF GmbH, a tissue section containing an HER2protein at an expression level of 2+ and subjected to trypsin digestiontreatment (manufactured by Pantomics, Inc.) was measured by SIMS underthe following conditions.

-   -   Primary ion: 25 kV Bi⁺, 0.6 pA (pulse current value),        macro-raster scan mode    -   Primary ion pulse frequency: 5 kHz (200 μs/shot)    -   Primary ion pulse width: about 0.8 ns    -   Primary ion beam diameter: about 0.8 μm    -   Measurement range: 4 mm×4 mm    -   Number of pixels used for measuring secondary ion: 256×256    -   Cumulative time: 512 shots per pixel, single scan (about 150        minutes)    -   Secondary ion detection mode: positive ion

The resultant SIMS data contains XY coordinate information representinga position and a mass spectrum per shot for each measured pixel. Forexample, for each measured pixel, the SIMS data contains, as measuredspectrum data, a peak (m/z=720.35) corresponding to a mass number inwhich one sodium atom adsorbs to one of the digestion fragments of theHER2 protein, and information on a peak component attributed to eachbiological tissue.

FIG. 11A shows the result obtained by subjecting the tissue sectioncontaining an HER2 protein at an expression level of 2+ (manufactured byPantomics, Inc.) to immunostaining for the HER2 protein, and observingthe tissue section with a light microscope. In FIG. 11A, a portion atwhich the HER2 protein is expressed at a higher level is displayedbrighter. It should be noted that, the sample subjected to the SIMSmeasurement and the sample subjected to the immunostaining are not thesame but are adjacent sections excised from the same lesion tissue(paraffin block).

FIG. 11B shows a peak distribution image (m/z=720.35) before theapplication of the technique of the present invention, and FIG. 11Cshows a peak distribution image after the application. Machine learningusing a decision tree is used for the preparation of reference data. Theimage data of FIG. 11A described above is used for the label decision oftraining data in that case, and 4,096 pieces of data are used as thetraining data. The number of peaks used for the generation of typicalspecific spectra is six in total, and m/z values corresponding to thepeaks are 692.35, 720.35, 932.63, 1,101.5, 1,128.6, and 1,326.4,respectively, three of which correspond to theoretical values for thedigestion fragments.

FIG. 12A and FIG. 12B are partially enlarged images of FIG. 11B and FIG.11C, respectively. FIG. 12A shows an image before the application ofthis technique, and FIG. 12B shows an image after the application. It isunderstood that the application of this technique provides reducednoise, an improved image contrast, and a sharper outline.

EXAMPLE 2

Hereinafter, Example 2 of the present invention is described. Althoughthe apparatus conditions and experiment conditions of this example arethe same as those in the case of Example 1, the Fisher's lineardiscriminant method was employed as the technique for machine learning.

FIG. 13A and FIG. 13B are images showing effects obtained in the case ofemploying the Fisher's linear discriminant method as the technique formachine learning and applying the technique of the present invention.FIG. 13A shows a peak distribution image (m/z=720.35) before theapplication of the technique of the present invention, and FIG. 13Bshows a peak distribution image after the application. 256 pieces ofdata are used as training data. The number of peaks used for the machinelearning is two in total, and m/z values corresponding to the peaks are692.35 and 1,101.5, respectively. It is understood that the applicationof this technique provides reduced noise, an improved image contrast,and a sharper outline.

The present invention can be utilized as a technique for reducing noisein measured spectrum data more effectively.

Other Embodiments

Aspects of the present invention can also be realized by a computer of asystem or apparatus (or devices such as a CPU or MPU) that reads out andexecutes a program recorded on a memory device to perform the functionsof the above-described embodiment(s), and by a method, the steps ofwhich are performed by a computer of a system or apparatus by, forexample, reading out and executing a program recorded on a memory deviceto perform the functions of the above-described embodiment(s). For thispurpose, the program is provided to the computer for example via anetwork or from a recording medium of various types serving as thememory device (e.g., computer-readable medium).

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2012-016429, filed Jan. 30, 2012, and Japanese Patent Application No.2013-005347, filed Jan. 16, 2013, which are hereby incorporated byreference herein in their entirety.

What is claimed is:
 1. A method of acquiring a biological tissue image,the method comprising the step of: reconstructing a biological tissueimage having reduced noise through use of a plurality of measuredspectrum data obtained by measuring respective regions of a biologicaltissue, wherein the reduction of the noise is performed through use of atechnique for machine learning utilizing reference data for the measuredspectrum data, wherein the reference data is generated throughutilization of training data, and wherein the reduction of the noise isperformed by generating a classifier through utilization of the trainingdata, dividing an entire spectrum into typical specific spectra throughuse of the classifier, and reconstructing an image from the typicalspecific spectra derived from the biological tissue.
 2. The methodaccording to claim 1, wherein the measured spectrum data is one of dataof optical spectrum in a range of ultraviolet, visible or infraredlight, Raman spectrum and mass spectrum.
 3. An apparatus forreconstructing a biological tissue image comprising a central processingunit and memory, said central processing unit and memory cooperating toexecute the method of claim
 1. 4. The apparatus according to claim 3,further comprising a detector for detecting a signal from a sample on asubstrate, and an image displaying member for displaying thereconstructed image.
 5. The apparatus according to claim 4, wherein thedetector obtains a two-dimensional mass spectrum.
 6. The methodaccording to claim 1, wherein the technique for machine learning isselected from the group consisting of the Fisher's linear discriminantmethod, a Support Vector Machine (SVM), a decision tree, and a randomforest method.
 7. The method according to claim 1, further comprising astep where components other than the typical specific spectra areexpressed by 0 with respect to the measured spectrum data.
 8. The methodaccording to claim 2, wherein the measured spectrum data is atwo-dimensional mass spectrum which utilizes a mass number (m/z) and astrength data corresponding to the peak of the mass spectrum.